School of Electrical and Electronic Engineering, University College Dublin, Ireland.
Insight Centre for Data Analytics, University College Dublin, Ireland.
Am J Speech Lang Pathol. 2024 May;33(3):1390-1405. doi: 10.1044/2024_AJSLP-23-00175. Epub 2024 Mar 26.
Changes in voice and speech are characteristic symptoms of Huntington's disease (HD). Objective methods for quantifying speech impairment that can be used across languages could facilitate assessment of disease progression and intervention strategies. The aim of this study was to analyze acoustic features to identify language-independent features that could be used to quantify speech dysfunction in English-, Spanish-, and Polish-speaking participants with HD.
Ninety participants with HD and 83 control participants performed sustained vowel, syllable repetition, and reading passage tasks recorded with previously validated methods using mobile devices. Language-independent features that differed between HD and controls were identified. Principal component analysis (PCA) and unsupervised clustering were applied to the language-independent features of the HD data set to identify subgroups within the HD data.
Forty-six language-independent acoustic features that were significantly different between control participants and participants with HD were identified. Following dimensionality reduction using PCA, four speech clusters were identified in the HD data set. Unified Huntington's Disease Rating Scale (UHDRS) total motor score, total functional capacity, and composite UHDRS were significantly different for pairwise comparisons of subgroups. The percentage of HD participants with higher dysarthria score and disease stage also increased across clusters.
The results support the application of acoustic features to objectively quantify speech impairment and disease severity in HD in multilanguage studies.
声音和言语的变化是亨廷顿病（HD）的特征性症状。能够跨语言使用的定量言语障碍的客观方法可以促进疾病进展和干预策略的评估。本研究的目的是分析声学特征，以确定可用于量化讲英语、西班牙语和波兰语的 HD 患者言语障碍的语言独立特征。
90 名 HD 患者和 83 名对照参与者使用先前经过验证的方法，使用移动设备完成了持续元音、音节重复和阅读短文任务。确定了 HD 患者和对照组之间存在差异的语言独立特征。对 HD 数据集的语言独立特征进行主成分分析（PCA）和无监督聚类，以识别 HD 数据中的亚组。
确定了 46 个语言独立的声学特征，这些特征在对照组和 HD 患者之间存在显著差异。使用 PCA 进行降维后，在 HD 数据集中识别出四个语音簇。统一亨廷顿病评定量表（UHDRS）总运动评分、总功能容量和复合 UHDRS 评分在亚组的两两比较中存在显著差异。言语障碍评分和疾病阶段较高的 HD 参与者的比例也随着聚类的增加而增加。
这些结果支持在多语言研究中应用声学特征客观地定量评估 HD 患者的言语障碍和疾病严重程度。
